Concerning fairness, [1]} discuss the effects of biases in medical ML and how biases like image acquisition, genetic variation, and intra-observer labeling result in healthcare disparities. They go on to argue that causal analysis in medical ML can greatly help mitigate such biases. Expanding on this argumentation, [2]} argue that information fusion is key to achieve greater transparency and safety in medical imaging ML applications. In a slightly different position paper, [3]} argue for the standardization of medical metadata in order to assist causal inference techniques in biomedical ML. Along similar lines, [4]} argues for the use of causal intuition when designing medical imaging datasets. Meanwhile, [5]} uses causal inference to estimate the necessity and sufficiency of the type and quantity of data to include in a medical imaging dataset in order to improve model performance under strict computational and financial constraints. In addition, [6]} contend that causal analysis can help alleviate biases and provide the necessary trust to medical imaging application in deep space manned missions.
